This book, subtitled "How to make good programming decisions", shows how to optimize the decisions that define your code by exploring the common mistakes and intentional tradeoffs made by expert developers. Tomasz Lelek and Jon Skeet explore real-world scenarios where poor understanding of tradeoffs lead to major problems down the road, so you can preempt your own mistakes with a more thoughtful approach to decision making.
<ASIN:1617299200>
Learn how code duplication impacts the coupling and evolution speed of your systems, and how simple-sounding requirements can have hidden nuances with respect to date and time information. Discover how to efficiently narrow your optimization scope according to 80/20 Pareto principles, and ensure consistency in your distributed systems. You’ll soon have built up the kind of knowledge base that only comes from years of experience.
Author: Tomasz Lelek and Jon Skeet Publisher: Manning Date: June 2022 Pages: 426 ISBN: 978-1617299209 Print: 1617299200 Kindle: B09YSNVV52 Audience: General Level: Intermediate Category: Methodology
- Reason about your systems to make intuitive and better design decisions
- Understand consequences and how to balance tradeoffs
- Pick the right library for your problem
- Thoroughly analyze all of your service’s dependencies
- Understand delivery semantics and how they influence distributed architecture
- Design and execute performance tests to detect code hot paths and validate a system’s SLA
- Detect and optimize hot paths in your code to focus optimization efforts on root causes
- Decide on a suitable data model for date/time handling to avoid common (but subtle) mistakes
- Reason about compatibility and versioning to prevent unexpected problems for API clients
- Understand tight/loose coupling and how it influences coordination of work between teams
- Clarify requirements until they are precise, easily implemented, and easily tested
- Optimize your APIs for friendly user experience
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Geometrical Vectors
Author: Gabriel Weinreich Publisher: University of Chicago Press Pages: 126 ISBN: 978-0226890487 Print: 0226890481 Kindle: B01EYG40HO Audience: Mathematicians, physicists and engineers. Rating: 5 Reviewer: Mike James Geometrical Vectors - are there any other kind?
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Artificial Intelligence, Machine Learning, and Deep Learning (Mercury Learning)
Author: Oswald Campesato Publisher: Mercury Learning Date: February 2020 Pages: 300 ISBN: 978-1683924678 Print: 1683924673 Kindle: B084P1K9YP Audience: Developers interested in machine learning Rating: 4 Reviewer: Mike James
Another AI/ML book - is there room for another one?
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